Most relevant x-ray image selection for hemodynamic simulation

ABSTRACT

A method and apparatus for selecting one or more diagnostic images to generate a physiological model are provided in which a set of candidate images is determined for review by a user, in particular by a physician. The candidate images are hereby determined using one or more target measures, such as a density measure, a motion measure, a deviation measure or the like, that have been derived for each diagnostic image of an X-ray angiography series and by analyzing said target measure. Subsequently, a suitability score that is based on the requirements of the physiological model that shall be generated from the selected candidate images is assigned to each candidate image.

FIELD OF THE INVENTION

The present invention relates to a method for image selection, acorresponding apparatus and a computer program. In particular, thepresent invention relates to a method which employs the use of asuitability score to automatically select one or more images from a setof candidate images whereby the suitability score is dependent on thesubsequent usage of the selected images, in particular on the kind offluid dynamics simulation to be performed on the basis of said selectedimages.

BACKGROUND OF THE INVENTION

Diagnostic images acquired using X-ray angiography provide an importanttool for obtaining information about the coronary arteries. These imagesallow to accurately evaluate a coronary disease by means of a variety ofdifferent approaches.

One such approach is the image-based “virtual” determination ofhemodynamic measures, such as the Fractional Flow Reserve (FFR) orInstantaneous Wave-Free Ratio (iFR). Both, FFR as well as iFR, are ameasure for the pressure drop of the blood along a vessel of interest,e.g. due to a stenosis in said vessel of interest. They may bedetermined as the ratio of the pressure distal the stenosis (P_(d)) tothe pressure in the aorta (P_(a)).

In the past, FFR and/or iFR measurements were typically performedinvasively, by measuring the pressure at a position distal from and aposition proximate to the lesion using a respective intravascularmeasurement device including a pressure sensor. For FFR, these invasivemeasurements have to be performed during maximal blood flow, i.e. underhyperemia, which may cause discomfort in the patient. In contrast, iFRmeasurements may be performed at rest during a specific period indiastole, thereby avoiding the necessity to induce hyperemia in thepatient.

In recent years, efforts have been taken to determine the FFR and/or iFRvalues non-invasively by means of the above-mentioned virtual approach.In accordance with the virtual approach, the fluid dynamics in thecoronary arteries of a patient are simulated on the basis of aphysiological model including a fluid dynamics model representing theblood flow through the vessel or vessels of interest.

SUMMARY OF THE INVENTION

It is an object of the present invention to provide a method of imageselection that allows to select the image or images most suitable forsuch physiological modelling with minimum user interaction in a reducedamount of time.

More specifically, it is an object of the invention to provide a methodand an apparatus that allows to automatically pre-select a set ofcandidate images from one or more series of diagnostic images to presentto a user, in particular a physician, such that the user has to review areduced amount of images. Even more particularly, it is an object of theinvention to provide a method and an apparatus which allows a user toquickly determine the suitability of a set of candidate images for afluid dynamics simulation of a particular kind.

This objective is achieved by a method of selecting one or morediagnostic images for generating a physiological model, the method beinga computer-implemented method comprising the steps of obtaining aplurality of diagnostic images of a target structure, deriving aplurality of target measures comprising at least one respective targetmeasure for each of the plurality of diagnostic images, analyzing theplurality of target measures to select a set of candidate images, andassigning a suitability score to each candidate image in the set ofcandidate images, the suitability score indicating a suitability of therespective candidate image for generating the physiological model.

In particular, one or more of these steps may be implemented as a unitof a corresponding apparatus. In an example, the obtaining step may beimplemented by means of an input unit, the deriving step may beimplemented by means of a computation unit, the analyzing step may beimplemented by means of an analyzation unit and the selecting step andthe step of assigning a suitability score may be implemented by means ofa selection unit.

The physiological model may be generated on the basis of a plurality ofdiagnostic images of the coronary vasculature that have been acquiredduring X-ray angiography. Preferably, not all diagnostic images thathave been acquired are used, but rather a selection thereof. Inparticular, the selection is performed such that the most suitablediagnostic images are used to generate the physiological model. Whichand how many diagnostic images are most suitable for a particularphysiological model hereby largely depends on the desired results of thefluid dynamics simulation.

The plurality of diagnostic images may particularly refer to a series ofdiagnostic images having been acquired by means of a medical imagingmodality. In some embodiments, the plurality of diagnostic images mayrefer to multiple series of diagnostic images acquired for one patient,such that the selection process is performed amongst multiple series.This allows to combine the information provided in different series ofdiagnostic images and may result in improved accuracy. The one or moreseries of diagnostic images may be acquired using any medical imagingmodality as long as it allows to visualize and identify the targetstructure. As an example for suitable medical imaging modalitiescomputed tomography, magnetic resonance imaging, ultrasound imaging orthe like are mentioned.

In some embodiments, the one or more series of diagnostic images mayparticularly refer to one or more series comprising a plurality of X-rayangiography images, in particular two-dimensional X-ray angiographyimages. For X-ray angiography, a contrast agent may be introduced intothe target structure. X-ray angiography imaging may then be performed onthe target structure during contrast agent inflow and contrast agentoutflow. That is, the series of X-ray angiography images may comprise asubset of diagnostic images representing the contrast agent inflow, asubset of diagnostic images representing the contrast agent filledtarget structure and a subset of diagnostic images representing thecontrast agent outflow. In this case, it may be beneficial to selectdiagnostic images as candidate images that have a low degree offoreshortening and low overlap. Further, in any case, the contrast agentfilling should be sufficient to provide suitable contrast.

A selected one or more diagnostic images may be used for generating aphysiological model. In that context, the term physiological model mayparticularly refer to a model of the target structure including ageometric model representing the target structure's geometry and/or afluid dynamics model representing the fluid dynamics through said targetstructure.

A target structure may particularly refer to a vessel of interest or aplurality of vessels of interest that shall be evaluated for aparticular patient. In some embodiments, the target structure mayparticularly refer to the coronary vasculature of a patient, saidcoronary vasculature including one or more vessels of interest.

In such a case, the physiological model may be generated from the one ormore selected diagnostic images by segmenting the vessel of interest orvessels of interest represented in the selected diagnostic images andgenerating the geometric model representing the vessel geometry of thevessel of interest and/or the fluid dynamics model representing thefluid dynamics through the vessel of interest. In some embodiments, thegeometric model may particularly correspond to a two-dimensional orquasi-three dimensional geometric model in which the third dimension isapproximated.

In some embodiments, the fluid dynamics model may particularlycorrespond to a lumped parameter fluid dynamics model. In such a lumpedparameter fluid dynamics model, the fluid dynamics of the vessels areapproximated by a topology of discrete entities. As an example, a vesseltree may be represented by a topology of resistor elements each having aparticular resistance. Accordingly, the outlet at a distal end of thevessel is also represented by a particular resistor element. Thisresistor element is then connected to ground such as to represent theconnection of the vessel to the venous system. Similarly, respectiveresistor elements may be connected to the series of resistor elementsrepresenting the vessel of interest, such as to represent the outflowfrom the vessel of interest at certain bifurcations. These resistorelements may typically also be connected to ground. Lumped parameterfluid dynamics models reduce the number of dimensions compared to otherapproaches such as Navier-Stokes or the like. Accordingly, using alumped parameter fluid dynamics model may allow for a simplifiedcalculation of the fluid dynamics inside the vessels and are thusparticularly beneficial in terms of processing time.

The term target measure may particularly refer to one or more propertiesof the target structure as represented in the at least one diagnosticimage. In this context, the term target measure may particularly referto a target structure density measure, i.e. a quantitative measureindicative of how much of the target structure is visible in therespective diagnostic image, a motion measure indicative of a motion ofthe target structure, e.g. between two consecutive diagnostic images, anoverlap measure indicating the amount of overlap in a diagnostic imageof the target structure, a visibility measure indicating the visibility,e.g. of the edges, of the target structure and/or a deviation featureindicating the deviation of a desired target acquisition time of aparticular diagnostic image. Each of these target measures may influencethe suitability of a particular diagnostic image for generating thephysiological model. As indicated, one or more such target measures maybe derived for each one of the diagnostic images. Subsequently, thetarget measure may be analyzed to select, from the plurality ofdiagnostic images, a set of appropriate candidates that may be used togenerate the physiological model. Hereby, it shall be understood thatthe influence that each of the target measures has on the suitabilitymay vary depending on the kind of physiological model to be generated.

As an example, in some embodiments, the physiological model is used toextract geometric parameters of the target structure, such as the vesseldimensions in a vasculature. In such a case a single diagnostic imagemay suffice for modeling. Hereby, in order to have the geometricmodeling be particularly accurate, factors such as segmentation accuracyand motion of the target structure, for example in case of a coronarytarget structure due to the cardiac phase, are important requirements.That is, the method will perform suitability scoring that takes intoaccount factors such as overlap in the target structure, contrast agentfilling, sharpness of the target structure's individual features,visibility of relevant bifurcations, as well as the motion phase, suchas the cardiac phase.

Based on this suitability scoring, the method may output one or morecandidate images that are considered particularly suitable for geometricmodeling. In some embodiments, the output may be provided to a user andthe user may then review these outputs and decide which candidate imageto be used for the modeling. In some embodiments, the output may beprovided directly to a processor capable of performing the modeling. Theprocessor may then use any of the suitable candidates at random or mayselect one of the candidate based on the suitability score.

In some embodiments, the physiological model including the fluiddynamics model is used for more complex hemodynamic simulations. Inthese cases, multiple diagnostic images may be necessary to generate thephysiological model. In some embodiments, at least one diagnostic imagerepresenting the contrast agent inflow into the target structure, atleast one diagnostic image representing the contrast agent outflow fromthe target structure and at least one diagnostic image representing thefully contrast agent filled target structure may be needed. These three(or more) particular diagnostic images at these three different phasesmay particularly be used to determine a measure of the inflow andoutflow speed of the contrast agent (from diagnostic images representingall three phases) and, in the case of a coronary vasculature as a targetstructure, a measure of the myocardial perfusion which may be used as aboundary condition for the hemodynamic simulation (from the diagnosticimages representing the outflow phase). Further, a diagnostic imagerepresenting the full filling phase may particularly allow to review thedetails of the target structure most accurately. In the case of avasculature, i.e., a vessel of interest to be modelled, this allows toidentify and determine possible branches from the vessel of interestand, thus, to integrate respective outlets into the physiological model.This overall enhances the accuracy of the hemodynamic simulation. Theabove-mentioned requirements for suitable candidate images such asoverlap, contrast agent filling, sharpness of the target structure'sindividual features as well as the motion phase are likewise to beaccounted for.

Depending on the requirements of the physiological model and, thus, thecomplexity of the hemodynamic simulation, the candidate images are thusidentified from the analysis of the target measure.

To that end, the term candidate image may refer to a diagnostic imagethat has been selected as a possible candidate for further physiologicalmodeling. In some embodiments, typically a set of candidate images isidentified, out of which the user or a processor may choose a desiredcandidate. In some embodiments, only a single candidate image may beidentified.

Each identified candidate image is then assigned a respectivesuitability score. The term suitability score may hereby particularlyrefer to a quantitative value indicating the suitability of therespective candidate image for the physiological modeling of the targetstructure. It shall be understood that the suitability score may not benecessarily be visualized and may also refer to a score that isdetermined solely for the purpose of internal processing by theprocessor implementing the physiological modeling.

In some embodiments, the suitability score may particularly refer to ascore assigned depending on the categorization of a candidate imagerepresenting the target structure during contrast agent inflow, contrastagent outflow or in the full filling phase and one or more respectivelyweighted target measures, such as the overlap measure identified for thetarget structure in that particular image, the motion measure determinedfor the target structure in said image, and the like. The weighting ofeach of these measures is adjusted in accordance with the influence ofsaid feature for the physiological model, i.e. the weighting may beadjusted in accordance with the specific requirements of the respectivephysiological model.

Providing a suitability score based on the requirements of therespective physiological model may thus be used to implement astraightforward and less time-consuming image selection which allows auser to identify a suitable image from a large plurality of imageswithout the need for much user interaction.

In some embodiments, the deriving of the plurality of target measurescomprises generating, for each of the plurality of diagnostic images, arespective processed image, the generating comprising assigning aplurality of quantitative values to a plurality of pixels of therespective diagnostic image, the quantitative value indicating aprobability that the pixel represents the target structure, and derivinga target structure density measure for each of the plurality ofdiagnostic images based on the plurality of quantitative values. In someembodiments, the target structure density measure for each of theplurality of images is derived based on a sum of the plurality ofquantitative values.

In some embodiments, the deriving the target measure comprises aderiving of a target structure density measure. Hereby, the term targetstructure density measure may particularly refer to a quantitativemeasure indicative of how much of the target structure is visible in therespective diagnostic image. That is, diagnostic images representingtarget structures with a large degree of contrast agent filling willhave a higher value for the target structure density measure anddiagnostic images representing target structures with a low degree ofcontrast agent filling will have a lower value for the target structuredensity measure.

It shall be understood that, in case of a series of diagnostic imagesacquired from contrast agent inflow to contrast agent outflow, thediagnostic images representing the fully contrast agent filled targetstructure will have a target structure density measure of a higher valuethan the diagnostic images representing the target structure during theinflow and outflow phase. Hereby, it shall be understood that there mayparticularly be one target structure density measure per diagnosticimage. In case the target structure corresponds to a coronaryvasculature, the target structure density measure may particularlyindicate the amount of the vessels visible in each particular diagnosticimage.

The deriving the target structure density measure is achieved byprocessing each of the plurality of diagnostic images using an imageprocessing algorithm specifically tuned for the target structure. Theoutput of this processing step is a processed image, i.e. one processedimage is generated for each diagnostic image.

In some embodiments, each pixel of the respective diagnostic image maybe assigned a quantitative value between 0 and 1 indicating a chance orprobability of said pixel belonging to the target structure to generatethe processed image. That is, pixels in the diagnostic images having ahigh probability of belonging to the target structure will be assignedwith a value close to 1 and pixels with a high probability of belongingto the background will be assigned with a value close to 0. Theprocessed image thus corresponds to a pixel map of values between 0 and1.

A target structure density measure may then be derived based on thispixel map by considering the quantitative values assigned to each ofthis pixel. In some embodiments, this may be achieved by determining thetarget structure density measure as a sum of all quantitative valueswithin one processed image. Given that pixels representing the targetstructure have a higher value than pixels representing background andgiven that the target structure becomes more visible the more contrastagent filling is provided in the target structure, summing thequantitative values to obtain the target structure density measure willyield a target structure density measure with a higher value for thecontrast filled target structure. Accordingly, a high value for thetarget structure density measure is directly proportionate to adiagnostic image in which the target structure is clearly visible.Further, given that less contrast agent will result in more background,the target structure density measure obtained as a sum from thequantitative values of the pixel map will be lower in the contrast agentinflow and contrast agent outflow phase, as well as in cases where thetarget structure is poorly visible for other reasons.

Given that the processed image is derived from the diagnostic image, thethus determined value for the target structure density measure may bedirectly correlated to the corresponding diagnostic image. As a result,the value obtained for the target structure density measure for eachdiagnostic image may be determined by first image processing thediagnostic image to obtain the processed image comprising the pixel mapand subsequently summing up the quantitative values for each pixel toobtain the target measure.

According to some embodiments, the selecting the set of candidate imagescomprises analyzing the derived target structure density measure as afunction of the measurement time, and obtaining, based on saidanalyzing, a first subset of candidate images representing a contrastagent inflow phase, a second subset of candidate images representing acontrast agent full filling phase, and a third subset of candidateimages representing a contrast agent outflow phase.

In some embodiments, the selecting of the candidate images may comprisean analyzing of the target structure density measure as a function ofmeasurement time. Hereby, the term measurement time may particularlyrefer to the time between the starting of the contrast agent inflow andthe contrast agent outflow, during which a plurality of diagnosticimages is acquired. The amount of data points for this analysis dependson the timing of the diagnostic imaging, i.e. there is one data pointindicating the target structure density measure for each diagnosticimage acquired.

The resulting curve of the target structure density measure over timeallows to determine, for each one of the diagnostic images, whether saiddiagnostic image represents the contrast agent inflow phase, thefull-filling phase or the contrast agent outflow phase. That is, uponselection of any of these images as candidates, the thus selectedcandidate images may be subdivided into three subsets corresponding tothe three phases.

As indicated herein above, the amount of images that need to be selectedfor the generation of the physiological model may depend on the kind ofphysiological model that shall be generated, i.e. on the kind and amountof information to be derived from said physiological model.

In case of more complex physiological modeling, it may be necessary toselect at least three—or more—diagnostic images. In some embodiments,each these at least three diagnostic images shall correspond to adifferent phase of the contrast agent flow, i.e. one diagnostic imagehas been obtained during contrast agent inflow, one image has beenobtained as the target structure has been fully filled with contrastagent and one diagnostic image has been obtained during contrast agentoutflow. That is, in some embodiments, the set of candidate objects,i.e. of diagnostic images selected as being suitable, may be subdividedinto three subsets, whereby each subset corresponds to one of theabove-mentioned contrast agent time phases, designated contrast agentinflow phase, full filling phase and contrast agent outflow phase,respectively.

Hereby, the contrast agent inflow phase corresponds to the time frominjection of the contrast agent until the contrast agent has fullyflowed into the target structure. The full filling phase corresponds tothe time during which the contrast agent remains in the targetstructure. The contrast agent outflow phase corresponds to the time fromthe end of the contrast agent injection until the contrast agent hasfully exited the target structure.

The identifying whether a particular diagnostic image—i.e. a potentialcandidate image—corresponds to a particular phase may particularly beperformed by considering the curve representing the plurality of targetstructure density measures as a function of the measurement time whichcovers all three phases. Hereby, the images belonging to the contrastagent inflow phase typically have a low to intermediate target structuredensity measure which increases with increasing measurement time. Theimages belonging to the full filling phase correspond to the targetstructure density measures having the peak values of the curve. Finally,the images belonging to the contrast agent outflow phase correspond tothe target structure density measures having a low to intermediate valuewhich is decreasing with increasing measurement time.

It shall be understood that the curve representing the target structuredensity measure as a function of measurement time may be displayed to auser, but may also correspond to a “virtual” curve internally processedin the apparatus for analyzing purposes only.

In the above-cited embodiment, the at least three diagnostic imagesselected as candidates for generating the physiological model eachcorrespond to a different filling phase of the contrast agent inflowinto the target structure. It shall be understood, though, that, inother embodiments, the candidates may be selected differently. In someembodiments, for example in cases where the target structure correspondsto a coronary vasculature, at least three diagnostic images representingdifferent heart phases may be selected. This may also be achieved byanalyzing the target structure density measure as a function of themeasurement time.

Particularly, in the case of a coronary vasculature, the curverepresenting the target structure density measure as a function ofmeasurement time may vary over time according to a cyclic variation thatis caused by the coronary contraction and expansion. These cyclicvariations further allow to determine a (consistent) heart phase for alldiagnostic images and, as such, allows a selection of the diagnosticimages as candidates based on the determined heart phase. Given that theheart phase may be an important factor for the accuracy of thephysiological modeling, the respectively determined heart phase may beconsidered in the suitability score for each candidate image. It shallbe understood, though, that while in some embodiments the heart phasemay be derived solely from the cyclic variation of the target structuredensity measure over time, in other embodiments the deriving may besupported by Electrocardiography (ECG) or by intracoronary pressuredata.

In some embodiments, the method may also comprise analyzing the firstsubset of candidate images, and/or analyzing the third set of candidateimages, and determining, for each one of the first subset of candidateimages and/or the third subset of candidate images, a visibility measureindicating a visibility of the target structure.

In some embodiments, it may be beneficial to select at least threeconsecutive images or images obtained in a very short time frame thatrepresent the contrast agent inflow phase or the contrast agent outflowphase as possible candidates, i.e. to further analyze the potentialcandidate images belonging to the first and the third subsets. Hereby,the analyzing shall be performed to identify three or more candidateimages having low foreshortening and low overlap, i.e. that are suitablewith respect to visibility of the target structure. Such visibility ofthe target structure may hereby particularly be expressed in terms of aso-called visibility measure.

Determining such a visibility measure for images from the inflow and/oroutflow phase of the contrast agent allows to select candidate imagesthat are particularly suitable for performing a flow velocity assessmentthrough the target structure, thereby allowing to derive importantboundary conditions for fluid dynamics modeling such as flow velocity,vessel wall resistance or the like. Similar to the cases above, this mayparticularly be achieved by analyzing the target structure densitymeasure (indicative of the amount of contrast agent inside the targetstructure) as a function of measurement time.

In some embodiments, the deriving the plurality of target measurescomprises identifying, for each of the plurality of diagnostic images, amotion measure indicative of a motion of the target structure. In someembodiments, the motion measure is identified by determining, for eachof the plurality of diagnostic images, the corresponding processedimage, and analyzing the processed images as a function of measurementtime, wherein the analyzing comprises subtracting two consecutiveprocessed images from one another to determine the motion feature.

In some embodiments, the processed images may also be used to determinethe extent of motion of the target structure. This allows to avoidselection of candidate images representing large motion of the targetstructure and, thus, may even comprise motion blur. For this purpose,the processed images are regarded as a function of the measurement time.This is possible by mapping each processed image to its correspondingdiagnostic image that has been obtained at a particular point inmeasurement time. Thus, the processed images are ordered based on theirmeasurement time, i.e. a series of processed images is obtained. Then,the pixel maps represented in neighboring (consecutive) processed imagesmay be subtracted from one another. Hereby, a large mean absolutedifference may indicate that large motion has occurred between the twocorresponding diagnostic images. This large mean absolute difference maybe used as a motion measure indicative of the motion of said targetstructure. The larger the value of the motion measure, the more motionis to be expected.

This concept is particularly important in the case of a coronary targetstructure. The cardiac cycle introduces a coronary motion in thecoronary vasculature that may influence the suitability of a particulardiagnostic image for physiological modeling. Accordingly, diagnosticimages identified as belonging to a particular phase of the cardiaccycle, typically the end diastole, may be preferred over otherdiagnostic images in terms of motion.

According to some embodiments, the deriving of the plurality of targetmeasures comprises identifying, for each of the plurality of diagnosticimages, an overlap measure indicative of an overlap in the targetstructure.

Another factor that may influence the accuracy of the physiologicalmodeling is the overlap of the target structure. As an example, in casethe target structure belongs to a (coronary) vasculature, multiplevessels that are visible in the (two-dimensional) diagnostic images mayonly be partially be visible due to vessel overlap from the particulardirection from which the diagnostic image was taken.

Accordingly, the amount of overlap in a particular diagnostic image mayalso be considered in the suitability score in terms of a so-calledoverlap measure. Overlaps may be detected, automatically, by imageprocessing using a target structure specific algorithm and/or manually.In the case of a (coronary) vasculature as the target structure,overlaps may for example be detected by detecting closed loops in thepixel maps represented in the processed images. This is the case sincethe pixel maps distinguish between background and structure only. Aclosed loop of higher quantitative values indicating the presence ofstructure allows to conclude that there are at least two vesselsoverlapping (since a vessel typically has an inflow and an outflow). Insome embodiments, closest loops may particularly be identified byperforming contour tracing within the target structure map in theprocessed image. In some embodiments, orientation scores may be used todetermine the closest loops directly from the diagnostic images.

In some embodiments, the deriving of the plurality of target measurescomprises receiving additional procedural information, for each of theplurality of diagnostic images. The additional procedural informationmay comprise information of administered medications that may influencethe diagnostic images or the physiological modeling. Examples of suchmedications are adenosine, nitroglycerin, or acetylcholine. Additionalprocedural information may further comprise sensor readouts, such asinformation about pressure, (blood) flow, ECG, or other information thatmay be acquired along with, at some time before or after acquisition ofthe diagnostic images. Based on the additional procedural information,further target measures like heart phase, coronary resting state,coronary hyperemic state, or others may be derived.

It shall be understood that the additional procedural information itselfmay also be regarded as an additional target measure. As an example,diagnostic images that were acquired without administration ofnitroglycerin, or too long after said administration may have theirsuitability score lowered based on this, due to the limited and possiblyinconsistent dilation of the coronary arteries. In some embodiments thediagnostic images may only receive a suitability score above 0 if theywere acquired directly after an intracoronary adenosine injection sincethis may be a necessary requirement for the corresponding physiologicalmodeling.

In some embodiments, the deriving of the plurality of target measurescomprises identifying, for each of the plurality of diagnostic images, adeviation measure indicative of a deviation from a desired targetacquisition time.

In some embodiments, it may be beneficial to select diagnostic imagesthat have been acquired at a particular target acquisition time, inparticular when the target structure shows varying motion over time. Asan example, if the target structure comprises a coronary vasculature,the cardiac cycle induces cyclic variation in the motion of thevasculature. In this case, a particular target acquisition time may bedetermined that takes account of this cyclic variation. That is,diagnostic images acquired at this target acquisition time shall befavored over diagnostic images acquired at a different measurement time.

The target acquisition time may thus correspond to an optimum phase inthe cardiac cycle. In some specific embodiments, the optimum phase mayfor example correspond to the end of the diastole. In other embodiments,other phases may be also be used, in particular when previously useddiagnostic images have been used in a different phase than the end ofthe diastole. In this case, the phase to be used for the diagnosticimages shall correspond to the phase previously used. That is, the phaseconsistency over all images should be maintained.

To that end, the diagnostic images not obtained at the targetacquisition time need not necessarily be less suitable for physiologicalmodeling. As an example, they may exhibit better contrast or the likewhich makes them more suitable than the diagnostic image obtained at thetarget acquisition time. As such, a respective image-specific value forthe deviation measure is introduced into the suitability score. Thedeviation measure is an indicator as to the distance from the selectedoptimal phase, i.e. the one or more target acquisition times at whichthe optimum phase is reached. In the case of the coronary physiology,the deviation measure is an indicator of the distance from the optimalstage in the cardiac cycle.

According to some embodiments, the suitability score is based on aweighted sum of the one or more target measures. In some specificembodiments, the suitability score may particularly be based on aweighted sum of the motion measure and/or the overlap measure and/or thedeviation measure. In some embodiments, the respective weighting factorsare adjusted based on one or more hemodynamic parameters to be modelledusing the physiological model to be generated based on one or moreimages to be selected from the set of candidate images.

In some embodiments, the suitability score indicating as to whether ornot a candidate image is particularly suited for generating thephysiological model may be derived from a weighted sum of some or all ofthe above-mentioned measures. In some embodiments, the suitability scoremay be determined according to

S _(inflow)=χ·(1−w ₁ ·O−w ₂ ·M−w ₃·θ)

Hereby, the term χ ∈ (0, 1) corresponds to an indicator determined onthe basis of the target structure density measure, whereby the indicatorindicates if the target structure density measure has identified thecorresponding diagnostic image as an image of the first subsetrepresenting the contrast agent inflow phase, an image of the secondsubset representing the contrast agent full filling phase or an image ofthe third subset representing the contrast agent outflow phase. The termw₁·0 corresponds to the weighted overlap measure, the term w₂·Mcorresponds to the weighted motion measure and the term w₃·θ correspondsto the weighted deviation measure.

In some embodiments, the weighting factors w₁, w₂ and w₃ are adjusteddepending on the requirements of the physiological model, i.e. thepurpose for which the physiological model shall be used. As an example,when selecting an image from the contrast agent inflow phase as areference for the inflow speed, the weighting factor w₁ may be setrather small since the vessel overlap will not be significant at theearly contrast agent inflow phase. Accordingly, the overlap measure maybe weighted less than the motion measure and/or the deviation measure.

In some embodiments, the (pre-)selected candidate images selected by themethod may be presented to a user, in particular a physician, for finalselection. In order to ease selection, the candidate images mayparticularly be presented along with a graphical representation of theirsuitability score. It shall be understood that the suitability scorepresented is always specific to the respective physiological model. Thatis, if the physiological model is used for a different purpose, thesuitability score may change.

The suitability score may correspond to a graphical representation of anumerical value shown in or alongside the respective candidate image. Insome embodiments, the suitability score may also correspond to acolor-coded scale indicating the suitability. Other manners ofrepresenting the different suitability scores for each diagnostic imagemay also be envisioned. By presenting the score in a visual manner, theuser may more easily select the images from the candidate images.

In another aspect, an apparatus for selecting one or more diagnosticimages for generating a fluid dynamics model, comprising an input unitconfigured to obtain a plurality of diagnostic images of a targetstructure, a computation unit configured to derive a plurality of targetmeasures comprising at least one respective target measure for each ofthe plurality of diagnostic images, an analyzation unit configured toanalyze the plurality of target measures, and a selection unitconfigured to select a set of candidate images based on the analyzing ofthe plurality of target measures and to assign a suitability score toeach candidate image in the set of candidate images, the suitabilitystore indicating a suitability of the respective candidate image forgenerating the fluid dynamics model. According to yet anotherembodiment, the selection unit comprises a classifier that has beentrained using a training data set correlating one or more diagnosticimages with correspondingly measured hemodynamic parameter data.

In some embodiments, an apparatus is provided which is configured toexecute the method as described herein above. For that purpose, theapparatus may comprise an input unit, a computation unit, an analyzationunit and a selection unit. Further, in some embodiments, the apparatusmay comprise a display unit, such as a liquid crystal display or thelike to display a graphical representation of the candidate imagesand/or their respective suitability score to a user. In someembodiments, the apparatus may further comprise or be communicativelyconnected to a modeling unit which is used to generate a physiologicalmodel from the one or more diagnostic images that have been presented ascandidate images particularly suitable for the physiological model andhave accordingly be selected by the user. Based on the physiologicalmodel one or more hemodynamic parameters, such as pressure, flowvelocity, or the like may be simulated, i.e. modeled, for example toperform virtual FFR or iFR.

To that end, the modeling unit may generate the physiological model bysegmenting the vessel of interest or vessels of interest represented inthe selected diagnostic images and generating a geometric modelrepresenting the vessel geometry of the vessel of interest and a fluiddynamics model representing the fluid dynamics through the vessel ofinterest.

Further, apart from using a programmed algorithm to select the candidateimages, it is also possible to provide the apparatus and, in particular,the selection unit with a trained classifier and to use machine learningfor image selection. For this purpose, the classifier implementing themachine learning algorithm may be trained from user behavior, i.e. maybe trained by tracing the preferred user selection of the candidateimages.

Alternatively, or additionally, the classifier may be trained using atraining data set. The training dataset may hereby particularly compriseone or more values for measured hemodynamic parameter data, such aspressure values or FFR values or the like, that are correlated withrespective diagnostic images. This combination of a diagnostic image anda corresponding hemodynamic parameter value allows to train the bestdiagnostic image with respect to contrast agent filling, cardiac phaseand the like. That is, these factors may be trained by training againstmeasured data.

In some embodiments, the classifier may also be trained to derive thetarget structure density measure and/or the motion measure and/or theoverlap measure and/or the deviation measure based on a training againsta measured dataset. In some embodiment, the trained classifier mayparticularly be trained for overlap detection based on a simulatedtraining dataset to determine the overlap measure.

In a further aspect, a computer program for performing the above-citedmethod is provided, which, when executed by a processing unit, isadapted to control an apparatus as described above. In an even furtheraspect, a computer-readable medium is provided having stored thereon theabove-cited computer program.

It shall be understood that the method of claim 1, the apparatus ofclaim 12, the computer program of claim 14 and the computer-readablemedium of claim 15 have similar and/or identical preferred embodiments,in particular, as defined in the dependent claims.

It shall be understood that a preferred embodiment of the presentinvention can also be any combination of the dependent claims or aboveembodiments with the respective independent claim.

These and other aspects of the invention will be apparent from andelucidated with reference to the embodiments described hereinafter.

BRIEF DESCRIPTION OF THE DRAWINGS

In the following drawings:

FIG. 1 schematically illustrates an apparatus for image selectionaccording to an exemplary embodiment.

FIG. 2 illustrates an exemplary graphical representation of theanalyzing of the target measure as a function of time according to anembodiment.

FIG. 3 illustrates an exemplary selection method according to anembodiment.

DETAILED DESCRIPTION OF EMBODIMENTS

The illustration in the drawings is schematically. In differentdrawings, similar or identical elements are provided with the samereference numerals.

FIG. 1 represents schematically an exemplary embodiment of an apparatus1 for performing image selection from a plurality of diagnostic imagesto obtain one or more candidate images for generating a physiologicalmodel. Apparatus 1 comprises an input unit 100, a computation unit 200,an analyzation unit 300 and a selection unit 400. Further, apparatus 1is communicatively connected to a display unit 500. Display unit 500 isconnected to input means 501 and further communicates with modeling unit2.

In the exemplary embodiment according to FIG. 1, input unit 100 isconfigured to receive a plurality of diagnostic images 10 and to providethe diagnostic images 10 to computation unit 200. The diagnostic images10 correspond to X-ray angiography images. It shall be understood,however, that the diagnostic images 10 may also be acquired using adifferent imaging modality.

Computation unit 200 is then configured to perform image processing onthe diagnostic images 10. Hereby, the image processing algorithm used bycomputation unit 200 is specifically tuned for the target structurewhich, in the exemplary embodiment according to FIG. 1, corresponds to acoronary vasculature. The processed image comprises a vessel map for thecoronary vasculature that highlights the vessel structures. This isachieved by assigning a pixel value indicative of the probability ofsaid pixel belonging to a vessel to each pixel in the diagnostic image.That is, a high pixel value (close to 1) indicates that a pixel mostprobably belongs to a vessel representation and a low pixel value (closeto 0) indicates that a pixel most probably belongs to a backgroundpixel.

In the exemplary embodiment according to FIG. 1, computation unit 200then analyzes the received processed images by determining a sum of thepixel values per processed image. This sum corresponds to the targetstructure density measure. Accordingly, a processed image having manypixels with a higher value (i.e. many pixels belonging to a vesselrepresentation) have a higher target structure density measure than therest. Typically, a higher target structure density measure alsocorresponds to a higher degree of contrast agent filling since at alower degree of contrast agent filling many vessel structures would notbe visible. Correspondingly, the target structure density measure forimages having a lower degree of contrast agent filling of the vesselswill be lower.

In the exemplary embodiment according to FIG. 1, computation unit 200may then provide the target structure density measure, optionally alongwith the diagnostic images and/or the processed images to analyzationunit 300.

Analyzation unit 300 then analyzes the target structure density measureas a function of measurement time. That is, analyzation unit 300determines, for each diagnostic image, the point of time at which theparticular diagnostic image was obtained and correlates said points intime to the corresponding target structure density measure for theparticular diagnostic image. In that context, the term measurement timeparticularly corresponds to the time from early contrast agent inflow tolate contrast agent outflow from the vasculature.

Based on the curve of the target structure density measure, analyzationunit 300 may then distribute the diagnostic images into three subsets ofimages, namely images belonging to the early contrast agent inflowphase, images belonging to the contrast agent full filling phase andimages belonging to the contrast agent outflow phase may be identified.

To that end, FIG. 2 schematically illustrates a graph 20 of the targetstructure density measure D as function of measurement time t. Below inFIG. 2, a plurality of diagnostic images 10 are shown, the pluralitycomprising diagnostic images 11, 12, 13 and 14. In the particularembodiment according to FIG. 2, the diagnostic images 11, 12, 13 and 14correspond to two-dimensional X-ray angiography images. The targetstructure density measure D corresponds to a density measure indicatingthe vessel density in each of the diagnostic images.

The target structure density measure D for diagnostic image 11 isindicated as 21 in the graph 20, the target structure density measure Dfor diagnostic image 12 is indicated as 22, the target structure densitymeasure D for diagnostic image 13 is indicated as 23 and the targetstructure density measure D for diagnostic image 14 is indicated as 24.Based on these target structure density measures, the diagnostic images11, 12, 13 and 14 may be assigned to one of the three different phasesindicated herein above.

In the exemplary embodiment according to FIG. 2, the target structuredensity measure 21 for diagnostic image 11 is in the low to intermediaterange and increasing. Thus, the diagnostic image 11 belongs to the earlycontrast agent inflow phase. Similarly, the target structure densitymeasure 22 appears to indicate the contrast agent inflow phase fordiagnostic image 12. The target structure density measures 23 and 24 arein the high range. Thus, corresponding diagnostic images 13 and 14 maybe considered as belonging to the contrast agent full filling phase.

As may also be appreciated from graph 20, the values for targetstructure density measure D show a cyclic variation over time. This isdue to the cardiac phase. In some embodiments, analyzation unit 300 mayuse said cyclic variation to derive the heart phase. This allows todetermine a consistent heart phase for all candidate images.

Going back to FIG. 1, analyzation unit 300 may thus use the curverepresenting target structure density measure D as a function of time tto determine, for the diagnostic images 10 whether the respective imagesbelongs to the contrast agent inflow phase, the contrast agent fullfilling phase or the contrast agent outflow phase. Further, analyzationunit 300 may use the cyclic variation of the curve to determine oneconsistent heart phase for all diagnostic images.

In the exemplary embodiment according to FIG. 1, analyzation unit 300may further be configured to determine a motion measure M. For thatpurpose, analyzation unit 300 may be configured to use the processedimages including the vessel mapping by subtracting two neighboringvessel maps, i.e. the vessel maps represented in two processed imagesderived from two consecutively obtained diagnostic images. By means ofsubtracting two neighboring images, a mean absolute difference of theneighboring vessel maps may be determined which may be used as a motionmeasure M. If the value of the mean absolute difference is large, the(coronary) motion is large, whereas if the value is small, the motionmay also be assumed to be small. That is, a higher value for motionmeasure M indicates more motion than a smaller value.

Analyzation unit 300 may further be configured to determine an overlapmeasure O. In the exemplary embodiment according to FIG. 1, analyzationunit 300 may be configured to identify, in each map as represented inthe processed images, closed loops. These closed loops may be consideredto indicate overlapping vessels. Based on these closed loops,analyzation unit 300 may then determine, for each processed image, anoverlap measure O indicating the amount of overlap in the correspondingdiagnostic image 10.

In the exemplary embodiment according to FIG. 1, analyzation unit 300may then provide the plurality of diagnostic images, their correspondingtarget structure density measures and motion as well as overlap measuresand, optionally, the plurality of processed images to selection unit400. Selection unit 400 may receive the plurality of diagnostic images,the target measures, the motion measures and the overlap measures aswell as the plurality of processed images (if provided) and may usethese information to determine, for each diagnostic image of theplurality of diagnostic images, a respective suitability score,indicating the diagnostic image's suitability for a particular purpose.In the embodiment according to FIG. 1, those diagnostic images shall beselected by selection unit 400 which are most suitable for generating aphysiological model including a geometric model and a fluid dynamicsmodel of the coronary vasculature for the purpose of deriving one ormore hemodynamic parameters for said coronary vasculature as targetstructure.

In the exemplary embodiment according to FIG. 1, selection unit 400calculates the suitability score S according to

S=χ·(1−w ₁ ·O−w ₂ ·M−w ₃·θ)

whereby the term χ ∈ (0, 1) corresponds to an indicator determined onthe basis of the target structure density measure, said indicatorindicating if the target structure density measure has identified thecorresponding diagnostic image as an image of the first subsetrepresenting the contrast agent inflow phase, an image of the secondsubset representing the contrast agent full filling phase or an image ofthe third subset representing the contrast agent outflow phase. The termw₁·O corresponds to a product of the overlaying feature with acorresponding weighting factor. Further, the term w₂·M corresponds tothe product of the motion measure with a corresponding weighted factor.

In the exemplary embodiment according to FIG. 1, in which the targetstructure is a coronary vasculature, the term w₃·θ is furtherconsidered. The θ corresponds to a deviation measure indicating thedistance of the particular image from the optimal phase in the cardiaccycle. In the embodiment of FIG. 1, this optimal phase corresponds tothe end diastole. Accordingly, the deviation measure θ indicates thedistance from said end of diastole. The factor w₃ corresponds to aweighting factor for said deviation measure. In the embodiment accordingto FIG. 1, the weighting factors w_(i), w₂ and w₃ are adjusted dependingon the requirements for the image-based physiological model. As anexample, a reference for the inflow speed is needed for the fluiddynamics model. In this case, the weighting factor w₁ for the overlapmeasure may be set rather small. This is the case since, in the earlycontrast agent inflow phase, only little contrast agent is inside thevessels and, thus, the overlap of the vessels may be negligible. Incontrast, when selecting an image from the contrast agent full fillingphase, the overlap measure shall be weighted higher, since, at thisstage, the vessel overlap is more significant.

The suitability score S may thus be determined as the product of theindicator for the target structure density measure times a factordetermined as 1 minus a weighted sum of the motion measure, the overlapmeasure and the deviation measure. The resulting score is thus higherthe smaller the influence of said features. A high suitability scoretherefore indicates a high chance that the diagnostic image may renderpromising results.

In the embodiment according to FIG. 1, computation unit 400 determinesthe suitability scores S to derive a set of candidate images andprovides these candidate images along with their respective suitabilityscores to display unit 500. Display unit 500 generates a graphicalrepresentation of each of the received candidate images and displayssaid graphical representation to the user. Optionally, the suitabilityscore may be displayed alongside the respective candidate image.

The user may thus browse through a set of candidate images automaticallypre-selected based on their suitability for the physiological modeling.This allows the user to review these candidate pictures only, as theyare objectively the best images available.

The user may then, via user interface 501, select one or more diagnosticimages (based on a visual inspection and/or the suitability score) andprompt the display device to provide the (finally) selected diagnosticimages to modeling unit 2. Modeling unit 2 then uses the one or moreselected images to generate a physiological model including a geometricmodel and a fluid dynamics model. The generated model may then beprovided to the display device 500 again and a graphical representationthereof may be presented to the user.

FIG. 2 represents schematically a flow chart for a method for imageselection according to an embodiment. In step S101, a plurality ofdiagnostic images 10 are received at input unit 100. In step S102, inputunit 100 provides these diagnostic images 10 to computation unit 200.

In step S201, the diagnostic images 10 are received at computation unit200. In step S202, computation unit 200 processes the diagnostic imagesusing an image processing algorithm that is specifically tuned for thetarget structure which, in the exemplary embodiment presented herein,corresponds to a coronary vasculature. By means of the processing, aprocessed image is obtained for each diagnostic image that comprises avessel map for the coronary vasculature. This is achieved by assigning apixel value indicative of the probability of said pixel belonging to avessel to each pixel in the diagnostic image. In step S203, a sum of thepixel values of the pixels in each processed image is calculated todetermine the target structure density measure. In step S204, the targetstructure density measure for each image, the plurality of processedimages derived from the plurality of diagnostic images and the pluralityof diagnostic images are provided to the analyzation unit 300.

In step S301, analyzation unit receives the plurality of processedimages, the plurality of diagnostic images and the target structuredensity measure. In step S302, analyzation unit 300 plots the targetstructure density measure as a function of measurement time for furtheranalysis. In step S303, analyzation unit 300 analyzes the curve of thetarget structure density measure as a function of time and identifies,based on said analysis, whether a respective image belongs to the earlycontrast agent inflow phase, to the contrast agent full filling phase orto the contrast agent outflow phase as described in relation to FIG. 2.In step S304, analyzation unit 300 further uses the curve of the targetstructure density measure to determine the heart phase at which eachdiagnostic image was obtained. This allows to provide a consistent heartphase for all candidate images that may be suggested to the user.

In step S305, analyzation unit 300 determines a motion measure M. In theexemplary embodiment according to FIG. 3, this is achieved bysubtracting the vessel mapping of two neighboring processed images toobtain a mean absolute difference of the neighboring vessel maps. Themean absolute difference may then be used as motion measure M, whereby alarger value indicates large coronary motion and a smaller valueindicates smaller coronary motion. It shall be understood that theoutput of step S305 shall comprise a motion measure value for eachdiagnostic image that has been considered.

In step S306, analyzation unit 300 further determine an overlap measure0 by identifying closed loops appearing in the respective vessel maps asshown in each of the plurality of processed images. Depending on howmany closed loops are identified per processed image, the overlapmeasure O is set to indicate the amount of overlap in one particulardiagnostic image which corresponds to the respective processed image. Itshall be understood that the output of step S306 typically comprises anoverlap measure value for each diagnostic image that has beenconsidered.

In step S307, analyzation unit 300 provides the plurality of diagnosticimages, their target structure density measures as well as theirdetermined motion and overlap measure and, optionally, the processedimage to selection unit 400.

In step S401, selection unit 400 receives the plurality of diagnosticimages, their respective target structure density measures, motion andoverlap measures and the plurality of processed images (if provided).

In step S402, selection unit 400 considers the requirements of theprocess the images are needed for. In the particular embodiment of FIG.2, the diagnostic images shall be used for generating a physiologicalmodel including a geometric model and a fluid dynamics model. Based onthese requirements, selection unit 400 sets respective weighting factorsfor each of the motion measure and the overlap measure. Further,selection unit derives, for each diagnostic image, a deviation measureindicating the distance from the optimal phase in the cardiac cycle anda corresponding weighting factor that also depends on the requirementsset out by the modeling process to the diagnostic images.

In step S403, selection unit 400 determines the suitability scores S asdescribed herein above in relation to FIG. 1 in order to (pre-)select aset of candidate images. In step S404, selection unit 400 provides thesecandidate images along with their respective suitability scores todisplay unit 500.

In step S501, display unit 500 receives the set of candidate images thathave been pre-selected and their respective suitability scores and, instep S502, generates a graphical representation of each candidate imagein the set of candidate images. In step S503, display unit 500 displaysthe graphical representation of each one from the set of candidateimages, which may optionally include the corresponding suitabilityscore, to the user.

In step S504, the user reviews the presented set of candidate images andselects one or more diagnostic as represented in the set of candidateimages. This selection prompts the selected one or more diagnosticimages to be provided to modeling unit 2 in step S601. In response tothe receiving of the one or more selected diagnostic images, modelingunit 2 generates a physiological model including a geometric model and afluid dynamics model for hemodynamic simulation. Thus, a method isenabled which allows to select diagnostic images in an efficient andquick manner without many user interactions necessary.

Although in the above-cited embodiments, the diagnostic images have beenacquired using X-ray angiography, it shall be understood that otherimaging modalities may likewise be used, such as computed tomography,ultra sound imaging, magnetic resonance imaging or the like.

Further, while in the above embodiments, the method was applied to acoronary vasculature, it shall be understood that the method may equallybe used for image selection of images of different target structures, inparticular, target structure representing different parts of the humanand/or animal body.

Also, while in the above-described embodiments, the selection has beenbased on the target structure density measure and a (weighted) motionmeasure, overlap feature and deviation measure, it shall be understoodthat further factors may be included into the selection, such as C-armangulation, aortic pressure values, absence of intravascular devicessuch as IVUS or guide wires, and/or the frame rate.

Further, while in the above-cited embodiments, the selection has beenperformed by a handcrafted algorithm, it shall be understood thatmachine learning based methods may also be used for image selection-

Other variations to the disclosed embodiments can be understood andeffected by those skilled in the art in practicing the claimedinvention, from a study of the drawings, the disclosure, and theappended claims. In the claims, the word “comprising” does not excludeother elements or steps, and the indefinite article “a” or “an” does notexclude a plurality.

A single unit or device may fulfill the functions of several itemsrecited in the claims. The mere fact that certain measures are recitedin mutually different dependent claims does not indicate that acombination of these measures cannot be used to advantage.

Procedures like the generating of the processed images, the deriving ofthe plurality of target measures, the analyzing of the plurality oftarget measures, the selecting of the candidate images and/or thedetermining and the assigning of the suitability score et ceteraperformed by one or several units or devices can be performed by anyother number of units or devices. These procedures in accordance withthe invention can hereby be implemented as program code means of acomputer program and/or as dedicated hardware.

A computer program may be stored/distributed on a suitable medium, suchas an optical storage medium or a solid-state medium, supplied togetherwith or as part of other hardware, but may also be distributed in otherforms, such as via the Internet or other wired or wirelesstelecommunication systems.

Any reference signs in the claims should not be construed as limitingthe scope.

The invention relates to a method of selecting one or more diagnosticimages for generating a fluid dynamics model, the method comprising thesteps of obtaining a plurality of diagnostic images of a targetstructure, deriving a plurality of target measures comprising at leastone respective target measure for each of the plurality of diagnosticimages, analyzing the plurality of target measures to select a set ofcandidate images, and assigning a suitability score to each candidateimage in the set of candidate images, the suitability store indicating asuitability of the respective candidate image for generating the fluiddynamics model.

By means of the method and apparatus for image selection, an automaticimage selection process may be established with allows to pre-select aset of candidate images such that the user may find the most suitablediagnostic images more quickly and with a reduced amount of userinteraction.

1. A computer-implemented method of selecting one or more diagnosticimages for generating a physiological model, the method comprising thesteps of: obtaining a plurality of diagnostic images of a targetstructure, deriving a plurality of target measures comprising at leastone respective target measure for each of the plurality of diagnosticimages, analyzing the plurality of target measures to select a set ofcandidate images, and assigning a suitability score to each candidateimage in the set of candidate images, the suitability score indicating asuitability of the respective candidate image for generating thephysiological model.
 2. The method according to claim 1, wherein thederiving of the plurality of target measures comprises: generating, foreach of the plurality of diagnostic images, a respective processedimage, the generating comprising assigning a plurality of quantitativevalues to a plurality of pixels of the respective diagnostic image, thequantitative value indicating a probability that the pixel representsthe target structure, and deriving a target structure density measurefor each of the plurality of diagnostic images based on the plurality ofquantitative values.
 3. The method according to claim 2, wherein thetarget structure density measure for each of the plurality of images isderived based on a sum of the plurality of quantitative values.
 4. Themethod according to claim 2, wherein the selecting the set of candidateimages comprises: analyzing the derived target structure density measureas a function of measurement time, and obtaining, based on saidanalyzing: a first subset of candidate images representing a contrastagent inflow phase; a second subset of candidate images representing acontrast agent full filling phase; and a third subset of candidateimages representing a contrast agent outflow phase.
 5. The methodaccording to claim 4, further comprising: analyzing the first subset ofcandidate images, and/or analyzing the third subset of candidate images,determining, for each one of the first subset of candidate images and/orthe third subset of candidate images, a visibility measure indicating avisibility of the target structure.
 6. The method according to claim 2,wherein the deriving of the plurality of target measures comprises:identifying, for each of the plurality of diagnostic images, a motionmeasure indicative of a motion of the target structure.
 7. The methodaccording to claim 6, wherein the motion feature is identified bydetermining, for each of the plurality of diagnostic images, thecorresponding processed image, and analyzing the processed images as afunction of measurement time, wherein the analyzing comprisessubtracting two consecutive processed images from one another todetermine the motion measure.
 8. The method according to claim 1,wherein the deriving of the plurality of target measures comprises:identifying, for each of the plurality of diagnostic images, an overlapmeasure indicative of an overlap in the target structure.
 9. The methodaccording to claim 1, wherein the deriving of the plurality of targetmeasures comprises: identifying, for each of the plurality of diagnosticimages, a deviation measure indicative of a deviation from a desiredtarget acquisition time.
 10. The method according to claim 1, whereinthe suitability score is based on a weighted sum of the one or morederived target measures.
 11. The method according to claim 10, wherebythe respective weighting factors are adjusted based on one or morehemodynamic parameters to be modelled using the physiological model tobe generated based on one or more images to be selected from the set ofcandidate images.
 12. An apparatus for selecting one or more diagnosticimages for generating a physiological model, comprising: an input unitconfigured to obtain a plurality of diagnostic images of a targetstructure, a computation unit configured to derive a plurality of targetmeasures comprising at least one respective target measure for each ofthe plurality of diagnostic images, an analyzation unit configured toanalyze the plurality of target measures, and a selection unitconfigured to select a set of candidate images based on the analyzing ofthe plurality of target measures and to assign a suitability score toeach candidate image in the set of candidate images, the suitabilitystore indicating a suitability of the respective candidate image forgenerating the physiological model.
 13. The apparatus according to claim12, wherein the selection unit comprises a classifier that has beentrained using a training data set correlating one or more diagnosticimages with correspondingly measured hemodynamic parameter data.
 14. Acomputer program for controlling an apparatus, which, when executed by aprocessing unit, is adapted to perform the method according to claim 1.15. A computer-readable medium having stored thereon the computerprogram according to claim 14.